Time Until Large Events#

import pandas as pd
import numpy as np
import datetime as dt

csv_file = "../datasets/Formatted_ETAS_Output.csv"
etas = pd.read_csv(csv_file, sep = ',', lineterminator='\n')
csv_file = "../datasets/All (1960-2023).csv"
usgs = pd.read_csv(csv_file, sep = ',', lineterminator='\n', dtype={'time':str})
Hide code cell output 
C:\Users\Vishal\AppData\Local\Temp\ipykernel_11588\2239509985.py:4: DtypeWarning: Columns (1,2,3,4,6,7,8,9,15,16,17,18) have mixed types. Specify dtype option on import or set low_memory=False.
  usgs = pd.read_csv(csv_file, sep = ',', lineterminator='\n', dtype={'time':str})

Data Filtering#

  1. Converting the date columns to datetime

  2. Date > 1960-01-01 and < 2023-01-01

  3. Longitude > -123 and < -113

  4. Latitude > 29 and < 39

Hide code cell source 
etas["Date"] = pd.to_datetime(etas["Date"], errors="coerce", format="%m/%d/%y")
etas.loc[etas["Date"].dt.year > pd.Timestamp.now().year, "Date"] -= pd.DateOffset(years=100)
etas = etas[(etas['Date'] > pd.to_datetime('1960-01-01')) & (etas['Date'] < pd.to_datetime('2023-01-01'))]

#filter the dataset by X > -123 and X < -113 and Y > 29 and Y < 39
etas = etas[etas['X'] > -123]
etas = etas[etas['X'] < -113]
etas = etas[etas['Y'] < 39]
etas = etas[etas['Y'] > 29]

etas.head()
Date Time Year X Y Magnitude Z\r
1 1960-01-02 0:08:49.00 1960.006125 -115.6222 33.0793 4.25 7.9322
2 1960-01-02 0:10:31.00 1960.007305 -115.6323 33.1220 3.03 8.4015
3 1960-01-02 0:10:32.00 1960.007320 -115.5851 33.0745 3.03 7.9678
4 1960-01-02 0:11:07.00 1960.007720 -115.6256 33.0290 3.08 7.9737
5 1960-01-02 0:11:17.00 1960.007840 -115.6050 33.0276 3.61 7.9322
Hide code cell source 
usgs["Date"] = pd.to_datetime(usgs["time"], errors="coerce").dt.strftime("%Y-%m-%d")
usgs.drop(columns=["time"], inplace=True)
usgs = usgs[(pd.to_datetime(usgs['Date']) > pd.to_datetime('1960-01-01')) & (pd.to_datetime(usgs['Date']) < pd.to_datetime('2023-01-01'))]

usgs['longitude'] = pd.to_numeric(usgs['longitude'], errors='coerce')
usgs['latitude'] = pd.to_numeric(usgs['latitude'], errors='coerce')
usgs['mag'] = pd.to_numeric(usgs['mag'], errors='coerce')

#filter the dataset by X > -123 and X < -113 and Y > 29 and Y < 39
usgs = usgs[usgs['longitude'] > -123]
usgs = usgs[usgs['longitude'] < -113]
usgs = usgs[usgs['latitude'] < 39]
usgs = usgs[usgs['latitude'] > 29]

time = []
for i in usgs['Date']:
    time.append(pd.to_datetime(i))
usgs['Date'] = time

usgs.head()
latitude longitude depth mag magType nst gap dmin rms net ... place type horizontalError depthError magError magNst status locationSource magSource\r Date
240 33.397500 -116.393333 3.88 4.14 mw 132 16 0.07391 0.19 ci ... 16 km N of Borrego Springs, CA earthquake 0.1 0.38 NaN 6 reviewed ci ci\r 2022-12-31
241 34.355667 -116.921833 4.73 3.47 mw 121 25 0.07845 0.15 ci ... 11km SSE of Lucerne Valley, CA earthquake 0.09 0.41 NaN 4 reviewed ci ci\r 2022-12-31
246 37.620167 -122.025000 3.82 3.34 mw 141 16 NaN 0.16 nc ... 3km N of Union City, CA earthquake 0.1 0.17 NaN 3 reviewed nc nc\r 2022-12-22
262 37.918167 -122.304000 5.48 3.57 mw 170 19 0.01598 0.15 nc ... 1km ENE of El Cerrito, CA earthquake 0.1 0.17 NaN 4 reviewed nc nc\r 2022-12-17
263 36.604667 -121.209333 8.88 3.28 ml 67 55 0.03812 0.09 nc ... 10km NW of Pinnacles, CA earthquake 0.14 0.28 0.129 72 reviewed nc nc\r 2022-12-13

5 rows × 22 columns

Data Grouping And Merging#

Data is grouped into 1 day chunks based on the max magnitude

max_mag_etas = pd.DataFrame(etas.groupby(etas['Date'].dt.to_period('S')).Magnitude.max())
max_mag_etas.reset_index(inplace=True)
max_mag_etas.head()
Date Magnitude
0 1960-01-02 00:00:00 4.25
1 1960-01-03 00:00:00 3.90
2 1960-01-04 00:00:00 4.24
3 1960-01-05 00:00:00 3.40
4 1960-01-06 00:00:00 3.47 
max_mag_usgs = pd.DataFrame(usgs.groupby(usgs['Date'].dt.to_period('S')).mag.max())
max_mag_usgs.reset_index(inplace=True)
max_mag_usgs.head()
Date mag
0 1960-01-02 00:00:00 4.04
1 1960-01-05 00:00:00 3.03
2 1960-01-07 00:00:00 3.64
3 1960-01-08 00:00:00 3.10
4 1960-01-11 00:00:00 3.79 
large_earthquake = 6.5

Large Events#

A label is added to Large Event data

large_mag_etas = max_mag_etas.copy()
large_mag_etas["Large Event"] = (large_mag_etas["Magnitude"] > large_earthquake).astype(int)
large_mag_etas["Date"] = large_mag_etas["Date"].dt.to_timestamp()

large_mag_etas = large_mag_etas.copy()

condition = large_mag_etas['Large Event'] == 1
subset = large_mag_etas.loc[condition].copy()
large_mag_etas.loc[condition, 'time_diff'] = subset['Date'].diff().dt.days
large_mag_etas.loc[0, 'time_diff'] = pd.NA

large_mag_usgs = max_mag_usgs.copy()
large_mag_usgs["Large Event"] = (large_mag_usgs["mag"] > large_earthquake).astype(int)
large_mag_usgs["Date"] = large_mag_usgs["Date"].dt.to_timestamp()

large_mag_usgs = large_mag_usgs.copy()

condition = large_mag_usgs['Large Event'] == 1
subset = large_mag_usgs.loc[condition].copy()
large_mag_usgs.loc[condition, 'time_diff'] = subset['Date'].diff().dt.days
large_mag_usgs.loc[0, 'time_diff'] = pd.NA

large_mag_etas.head()
Date Magnitude Large Event time_diff
0 1960-01-02 4.25 0 NaN
1 1960-01-03 3.90 0 NaN
2 1960-01-04 4.24 0 NaN
3 1960-01-05 3.40 0 NaN
4 1960-01-06 3.47 0 NaN 
large_mag_usgs.head()
Date mag Large Event time_diff
0 1960-01-02 4.04 0 NaN
1 1960-01-05 3.03 0 NaN
2 1960-01-07 3.64 0 NaN
3 1960-01-08 3.10 0 NaN
4 1960-01-11 3.79 0 NaN

Graphing Time Until Large Events#

import plotly.express as px
import plotly.graph_objects as go
Hide code cell source 
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=large_mag_etas['Date'],
    y=large_mag_etas['time_diff'],
    mode='markers',
    marker=dict(color='red', line=dict(color='black', width=1)),
    name='ETAS'
))

fig.add_trace(go.Scatter(
    x=large_mag_usgs['Date'],
    y=large_mag_usgs['time_diff'],
    mode='markers',
    marker=dict(color='blue', line=dict(color='black', width=1)),
    name='USGS'
))

fig.update_layout(
    title='Time Between Large Events -ETAS vs USGS',
    xaxis_title='Date',
    yaxis_title='Time Difference (Days)',
    legend=dict(x=0, y=1, traceorder='normal', orientation='h')  # Adjust legend position
)

fig.show()

Calcutating Energy#

Converting the magnitudes to energy through the formula: (1/1.5) * log(10^(1.5*mag))

const = (1/1.5)
large_mag_etas['Energy'] = 10**(1.5*large_mag_etas['Magnitude'])
large_mag_etas['Energy'] = np.log(large_mag_etas['Magnitude'])*const

const = (1/1.5)
large_mag_usgs['Energy'] = 10**(1.5*large_mag_usgs['mag'])
large_mag_usgs['Energy'] = np.log(large_mag_usgs['mag'])*const
Hide code cell source 
fig = go.Figure()

def add_cumulative_energy_plot(dataset, color, name_prefix):
    large_events = dataset[dataset["Large Event"] == 1]

    for i in range(len(large_events) - 1):
        start_date = large_events.iloc[i]["Date"]
        end_date = large_events.iloc[i + 1]["Date"]

        subset = dataset[(dataset["Date"] >= start_date) & (dataset["Date"] < end_date)]

        cumulative_energy = subset["Energy"].cumsum()

        fig.add_trace(go.Scatter(
            x=subset["Date"],
            y=cumulative_energy,
            mode='lines',
            name=f'{name_prefix} Event {i+1}-{i+2}',
            line=dict(color=color, width=2)
        ))
Hide code cell source 
# Add cumulative energy plots for USGS and ETAS datasets
add_cumulative_energy_plot(large_mag_usgs, 'green', 'USGS')
add_cumulative_energy_plot(large_mag_etas, 'blue', 'ETAS')

fig.update_layout(
    title='Cumulative Energy Between Large Events USGS vs ETAS',
    xaxis_title='Date',
    yaxis_title='Cumulative Energy',
)

fig.show()

# etas['Large'] = etas['Magnitude'] > large_earthquake

# # Find the indices of large earthquakes
# large_earthquake_indices = etas.index[etas['Large']].tolist()

# # Initialize lists to store the sum of energies
# sum_energies_usgs = []

# # Calculate sum of energies between consecutive large earthquakes
# for i in range(len(large_earthquake_indices) - 1):
#     start_index = large_earthquake_indices[i]
#     end_index = large_earthquake_indices[i + 1]

#     # Extract the relevant subset of data
#     subset = etas.loc[start_index:end_index]

#     # Calculate the sum of energies in this interval
#     interval_energy_sum = subset['Energy'].sum()

#     # Append the sum of energies to the list
#     sum_energies_usgs.append(interval_energy_sum)

# usgs['Large'] = usgs['mag'] > large_earthquake

# # Find the indices of large earthquakes
# large_earthquake_indices = usgs.index[usgs['Large']].tolist()

# # Initialize lists to store the sum of energies
# sum_energies_etas = []

# # Calculate sum of energies between consecutive large earthquakes
# for i in range(len(large_earthquake_indices) - 1):
#     start_index = large_earthquake_indices[i]
#     end_index = large_earthquake_indices[i + 1]

#     # Extract the relevant subset of data
#     subset = usgs.loc[start_index:end_index]

#     # Calculate the sum of energies in this interval
#     interval_energy_sum = subset['Energy'].sum()

#     # Append the sum of energies to the list
#     sum_energies_etas.append(interval_energy_sum)

# usgs_energy_sum = {'Large Earthquake Interval Index': range(len(sum_energies_usgs)), 'Sum of Energies In USGS': sum_energies_usgs}
# usgs_energy_sum = pd.DataFrame(usgs_energy_sum)

# etas_energy_sum = {'Large Earthquake Interval Index': range(len(sum_energies_etas)), 'Sum of Energies In ETAS': sum_energies_etas}
# etas_energy_sum = pd.DataFrame(etas_energy_sum)

# fig = go.Figure()

# fig.add_trace(go.Scatter(x=usgs_energy_sum['Large Earthquake Interval Index'], y=usgs_energy_sum['Sum of Energies In USGS'],
#                          mode='lines+markers', name='Sum of Energies In USGS'))

# fig.add_trace(go.Scatter(x=etas_energy_sum['Large Earthquake Interval Index'], y=etas_energy_sum['Sum of Energies In ETAS'],
#                          mode='lines+markers', name='Sum of Energies In ETAS', line=dict(color='red')))

# fig.update_layout(
#     xaxis=dict(title='Large Earthquake Interval Index'),
#     yaxis=dict(title='Sum of Energies'),
#     title='Sum of Energies between Large Earthquakes'
# )

# fig.show()